RNA-seq of Arabidopsis root growth responses to mechanical impedance
Cite this dataset
Lindsey, Keith et al. (2021). RNA-seq of Arabidopsis root growth responses to mechanical impedance [Dataset]. Dryad. https://doi.org/10.5061/dryad.wpzgmsbk0
1. The growth and development of root systems, essential for plant performance, is influenced by mechanical properties of the substrate in which the plants grow. Mechanical impedance, such as by compacted soil, can reduce root elongation and limit crop productivity.
2. To understand better the mechanisms involved in plant root responses to mechanical impedance stress, we investigated changes in the root transcriptome and hormone signalling responses of Arabidopsis to artificial root barrier systems in vitro.
3. We demonstrate that upon encountering a barrier, reduced Arabidopsis root growth and the characteristic 'step-like' growth pattern is due to a reduction in cell elongation associated with changes in signalling gene expression. Data from RNA-sequencing combined with reporter line and mutant studies identified essential roles for reactive oxygen species, ethylene and auxin signalling during the barrier response.
4. We propose a model in which early responses to mechanical impedance include reactive oxygen signalling integrated with ethylene and auxin responses to mediate root growth changes. Inhibition of ethylene responses allows improved growth in response to root impedance, an observation that may inform future crop breeding programmes.
20 mg of tissue was ground in liquid nitrogen using TissueLyser II (QIAGEN, Manchester, UK) and RNA extracted using the Qiagen ReliaPrepTM RNA Tissue Miniprep System. RNA quality was determined using the NanoDrop ND-1000 spectrophotometer (ThermoFisher Scientific) and Agilent 2200 TapeStation. Libraries were constructed from 100 ng and 1 μg total RNA using the NEBNext UltraTM Directional RNA Library Prep Kit for Illumina for use with the NEBNext Poly(A) mRNA Magnetic Isolation Module (NEB, Hitchin, UK). mRNA was isolated, fragmented and primed, cDNA was synthesised and end prep was performed. NEBNext Adaptor was ligated and the ligation reaction was purified using AMPure XP Beads. PCR enrichment of adaptor ligated DNA was conducted using NEBNext Multiplex Oligos for Illumina (Set 1, NEB#E7335). The PCR reaction was purified using Agencourt AMPure XP Beads. Library quality was then assessed using a DNA analysis ScreenTape on the Agilent Technologies 2200 TapeStation. qPCR was used for sample quantification using NEBNext® Library Quant Kit Quick Protocol Quant kit for Illumina. Samples were diluted to 10 nM. 7 μl of each 10 nM sample was pooled together and all were run on two lanes using an Illumina HiSeq2500 (DBS Genomics facility, Durham University). Approximately 30M unique paired-end 125bp reads were carried per sample. Primers were designed using Primer-BLAST (http://www.ncbi.nlm.nih.gov/tools/primer-blast/) and synthesised by MWG Eurofins (http://www.eurofinsdna.com/). FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) was used to assess read quality and Trimmomatic (Bolger et al., 2014) was used to cut down and remove low quality reads. Salmon (Patro et al., 2017) was used for quasi-mapping of reads against the AtRTD2-QUASI (Brown et al., 2017; Zhang et al., 2017) transcriptome and to estimate transcript-level abundances. The tximport R package (Soneson et al., 2016) was used to import transcript-level abundance, estimate counts and transcript lengths, and summarise into matrices for downstream analysis in R. Before differential expression analysis, low quality reads were filtered out of the data set. Only genes with a count per million of 0.744 in 6 or more samples were retained. The DESeq2 (Love et al., 2014) R package was used to estimate variance-mean dependence in count data and test for differential expression (using the negative binomial distribution model). A padj-value of ≤0.05 and a log2fold change of ≥0.5 were selected to identify differentially expressed genes (DEGs). The 3D RNA-Seq online App (Guo et al., 2019; Calixto et al., 2018) was used for independent verification of estimated DEGs and for differential alternative splicing analysis.
The data are in an Excel file and easily accessible.
Biotechnology and Biological Sciences Research Council, Award: BBS/B/0773X; BB/M011186/1